Robust Humanoid Contact Planning with Learned Zero- and One-Step Capturability Prediction

TL;DR

This paper introduces a search-based footstep planner for humanoid robots that uses learned neural networks to predict capturability, enabling the generation of more disturbance-robust contact sequences efficiently.

Contribution

It proposes a novel planning method that integrates neural network-based capturability prediction to enhance robustness against external disturbances.

Findings

Planner produces more disturbance-resilient footstep sequences.

Neural networks enable efficient multi-contact capturability prediction.

Approach outperforms conventional planners in challenging scenarios.

Abstract

Humanoid robots maintain balance and navigate by controlling the contact wrenches applied to the environment. While it is possible to plan dynamically-feasible motion that applies appropriate wrenches using existing methods, a humanoid may also be affected by external disturbances. Existing systems typically rely on controllers to reactively recover from disturbances. However, such controllers may fail when the robot cannot reach contacts capable of rejecting a given disturbance. In this paper, we propose a search-based footstep planner which aims to maximize the probability of the robot successfully reaching the goal without falling as a result of a disturbance. The planner considers not only the poses of the planned contact sequence, but also alternative contacts near the planned contact sequence that can be used to recover from external disturbances. Although this additional consideration significantly increases the computation load, we train neural networks to efficiently predict multi-contact zero-step and one-step capturability, which allows the planner to generate robust contact sequences efficiently. Our results show that our approach generates footstep sequences that are more robust to external disturbances than a conventional footstep planner in four challenging scenarios.